Paper: A Hierarchical Phrase-Based Model For Statistical Machine Translation

ACL ID P05-1033
Title A Hierarchical Phrase-Based Model For Statistical Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2005
Authors

We present a statistical phrase-based transla- tion model that uses hierarchical phrases— phrases that contain subphrases. The model is formally a synchronous context-free gram- mar but is learned from a bitext without any syntactic information. Thus it can be seen as a shift to the formal machinery of syntax- based translation systems without any lin- guistic commitment. In our experiments us- ing BLEU as a metric, the hierarchical phrase- based model achieves a relative improve- ment of 7.5% over Pharaoh, a state-of-the-art phrase-based system.